Optical network bandwidth resource allocation method and device, electronic equipment and medium

Abstract

The present disclosure provides a kind of optical network bandwidth resource allocation method, device, electronic equipment and medium, it is related to communication network technical field.The method is by obtaining the network node information of the optical network region to be allocated, the network node information of the optical network region to be allocated is input into the throughput prediction model trained in advance, and the predicted throughput of each network node in the optical network region to be allocated is output;According to the predicted throughput of each network node in the optical network region to be allocated, bandwidth resource allocation is carried out for each network node in the optical network region to be allocated.The present disclosure predicts the throughput of each network node in network region, and then carries out bandwidth resource allocation according to the predicted throughput of each network node, can realize the dynamic allocation of bandwidth resource, improves network resource utilization.

Description

Technical Field

[0001] This disclosure relates to the field of communication network technology, and in particular to a method, apparatus, electronic device and medium for allocating optical network bandwidth resources. Background Technology

[0002] The commercial application of optical networks is maturing, gradually supporting over 95% of global traffic load and exhibiting a trend towards openness and decoupling. The development of new optical network services, the increase in network connections, and the expansion of coverage lead to a large number of transmission network nodes distributed across different regions, resulting in significant regional differences in traffic characteristics. This causes operators to collect a large amount of information from different network layers, different network nodes, and different formats within the optical network. How to effectively process and utilize this information has attracted widespread attention, especially as it is crucial for operators to maintain stable network operation.

[0003] Among these challenges, the rational planning of limited optical network bandwidth resources is beneficial for enabling operators across the entire region to collaboratively manage all network nodes, saving human resources and reducing network operating costs. It also promotes the integrated development of optical networks with technologies such as cloud computing and computing power networks. Therefore, how to allocate limited optical network bandwidth resources in a way that avoids waste due to excessive bandwidth while ensuring normal transmission within the limited bandwidth has become a pressing technical problem.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, and medium for allocating bandwidth resources in optical networks, which at least to some extent overcomes the problems of limited bandwidth resources, inappropriate bandwidth allocation, and incomplete utilization of bandwidth resources in related technologies.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a method for allocating bandwidth resources in an optical network is provided, comprising: acquiring network node information of an optical network area to be allocated, wherein the network node information includes: node information and performance information of one or more network nodes in the optical network area to be allocated; inputting the network node information of the optical network area to be allocated into a pre-trained throughput prediction model, and outputting the predicted throughput of each network node in the optical network area to be allocated; and allocating bandwidth resources to each network node in the optical network area to be allocated based on the predicted throughput of each network node in the optical network area to be allocated.

[0008] In some embodiments, the predicted throughput is the throughput of network nodes within a predicted time period. The method further includes: obtaining prediction time configuration information, wherein the prediction configuration information is used to determine the prediction time period; wherein inputting the network node information of the optical network area to be allocated into a pre-trained throughput prediction model and outputting the predicted throughput of each network node in the optical network area to be allocated includes: inputting the prediction time configuration information and the network node information of the optical network area to be allocated into a pre-trained throughput prediction model and outputting the throughput of each network node in the optical network area to be allocated within the predicted time period.

[0009] In some embodiments, the throughput prediction model is further used to output the prediction confidence of each network node in the optical network area to be allocated; wherein, according to the predicted throughput of each network node in the optical network area to be allocated, bandwidth resource allocation is performed for each network node in the optical network area to be allocated, including: obtaining a confidence threshold pre-configured for each network node; determining whether the prediction confidence of each network node is greater than the corresponding confidence threshold; for one or more network nodes that are greater than the confidence threshold, determining whether the network node meets the preset bandwidth resource allocation conditions; and allocating bandwidth resources to one or more network nodes in the optical network area to be allocated that meet the preset bandwidth resource allocation conditions according to the predicted throughput of each network node in the optical network area to be allocated.

[0010] In some embodiments, before determining whether a network node meets the preset bandwidth resource allocation conditions, the method further includes: identifying one or more network nodes that do not meet the preset bandwidth resource allocation conditions; and outputting node information for each network node that does not meet the preset bandwidth resource allocation conditions.

[0011] In some embodiments, the aforementioned preset bandwidth resource allocation conditions include: whether the network node is a node with an adjustable network topology, and / or whether the predicted throughput of the network node exceeds the corresponding service bandwidth.

[0012] In some embodiments, the node information in any of the above optical network bandwidth resource allocation methods includes at least one of the following: node geographical location information, node topology information, and node network address information; the performance information includes at least one of the following: data transmission information and data reception information.

[0013] In some embodiments, the network node information of the optical network region to be allocated is input into a pre-trained throughput prediction model, and the predicted throughput of each network node in the optical network region to be allocated is output. This includes: generating a node information matrix and a performance information matrix corresponding to the optical network region to be allocated based on the network node information of the optical network region to be allocated, wherein the node information matrix contains the node information of each network node in the optical network region to be allocated, and the performance information matrix contains the performance information of each network node in the optical network region to be allocated; inputting the node information matrix and the performance information matrix corresponding to each network node in the optical network region to be allocated into the pre-trained throughput prediction model, and outputting a predicted throughput matrix and a prediction confidence matrix corresponding to the optical network region to be allocated, wherein the predicted throughput matrix contains the predicted throughput of each network node in the optical network region to be allocated, and the prediction confidence matrix contains the prediction confidence of each network node in the optical network region to be allocated.

[0014] According to another aspect of this disclosure, an optical network bandwidth resource allocation apparatus is also provided, comprising: an information acquisition module for acquiring network node information of an optical network area to be allocated, wherein the network node information includes node information and performance information of one or more network nodes in the optical network area to be allocated; a throughput prediction module for inputting the network node information of the optical network area to be allocated into a pre-trained throughput prediction model and outputting the predicted throughput of each network node in the optical network area to be allocated; and a bandwidth allocation module for allocating bandwidth resources to each network node in the optical network area to be allocated based on the predicted throughput of each network node in the optical network area to be allocated.

[0015] According to another aspect of this disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the optical network bandwidth resource allocation method of any one of the above methods by executing the executable instructions.

[0016] According to another aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the optical network bandwidth resource allocation method of any one of the above.

[0017] According to another aspect of this disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the optical network bandwidth resource allocation method of any one of the above.

[0018] The optical network bandwidth resource allocation method, apparatus, electronic device, and storage medium provided in the embodiments of this disclosure acquire network node information of the optical network area to be allocated, input the network node information of the optical network area to be allocated into a pre-trained throughput prediction model, and output the predicted throughput of each network node in the optical network area to be allocated; based on the predicted throughput of each network node in the optical network area to be allocated, bandwidth resources are allocated to each network node in the optical network area to be allocated. By predicting the throughput of each network node in the network area and then allocating bandwidth resources based on the predicted throughput of each network node, dynamic allocation of bandwidth resources can be achieved, reducing network operating costs and improving network resource utilization.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0021] Figure 1 This diagram illustrates an application scenario according to an embodiment of the present disclosure.

[0022] Figure 2 This diagram illustrates a flowchart of an optical network bandwidth resource allocation method according to an embodiment of the present disclosure.

[0023] Figure 3 This invention discloses a flowchart of another optical network bandwidth resource allocation method according to an embodiment of the present disclosure;

[0024] Figure 4 This invention discloses a flowchart of another optical network bandwidth resource allocation method according to an embodiment of the present disclosure;

[0025] Figure 5 This invention discloses a flowchart of another optical network bandwidth resource allocation method according to an embodiment of the present disclosure;

[0026] Figure 6 This invention discloses a flowchart of another optical network bandwidth resource allocation method according to an embodiment of the present disclosure;

[0027] Figure 7 This diagram illustrates an optical network bandwidth resource allocation device according to an embodiment of the present disclosure.

[0028] Figure 8 This diagram illustrates a structural block diagram of an electronic device according to an embodiment of the present disclosure.

[0029] Figure 9 A schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure is shown. Detailed Implementation

[0030] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] As mentioned in the background, the commercial application of optical communication networks is gradually maturing, but optical network bandwidth resources are limited. Sufficient bandwidth resources can provide faster and more stable network connections, offering users a better online experience. Therefore, how to allocate limited optical network bandwidth resources is beneficial not only for enabling operators across the entire region to collaboratively manage all network nodes, saving human resources and reducing network operating costs, but also for promoting the integrated development of optical networks with technologies such as cloud computing and computing power networks.

[0033] To address the aforementioned issues, this example implementation provides a method for allocating optical network bandwidth resources, thereby enabling the rational allocation of limited bandwidth resources and the efficient use of optical network bandwidth resources.

[0034] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0035] Figure 1 This illustration shows an application scenario diagram according to an embodiment of the present disclosure, such as... Figure 1 As shown, the optical network communication system 10 includes multiple network nodes 101, which are connected in pairs via optical fibers to form a network topology and constitute an optical network.

[0036] It should be noted that an optical network is a wide area network (WAN), metropolitan area network (MAN), or a newly built large-scale local area network (LAN) that uses optical fiber as the primary transmission medium. An optical network is a communication network used to exchange information between two ends via optical fiber cables; it is a network used for data communication. Data signals in optical fibers are transmitted in the form of optical pulses, and optical networks are used for this transmission. Optical networks are characterized by high transmission speeds and long transmission distances.

[0037] An optical network is a network structure that uses optical fiber for transmission. Figure 1 Network node 101 serves as both the source and destination for transmitting and receiving information. Network node 101 can be various electronic devices, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, augmented reality devices, virtual reality devices, etc.

[0038] In an optical network communication system 10, the network nodes 101 are connected via optical fibers, and multiple network nodes are connected together to represent the topology of the optical network. The topology of an optical network is not limited to... Figure 1 The mesh topology shown also includes:

[0039] Bus topology: In bus topology, network nodes are connected through a single trunk with the help of optical couplers. It is a convenient and efficient topology.

[0040] Ring topology: In a ring topology, a node is connected to its neighboring nodes, forming a closed path. Information transmitted in the form of light is sent from one node to another. Furthermore, optical couplers are installed within the network to couple the transmitted optical signals from one node to another.

[0041] Star topology: In a star connection, the nodes of the network are connected together through a central hub, which can be either active or passive. This central hub then controls and directs the optical signals transmitted within the optical network.

[0042] Mesh topology: i.e. Figure 1 The structure shown is a mesh topology where nodes in the network form arbitrary connections. These point-to-point connections can be changed according to the application, demonstrating the flexibility of a star topology, where other nodes can be used for signal transmission in the event of a node failure.

[0043] In some embodiments of this disclosure, the optical network communication system described above, and the network transmitted via optical fiber, include, but are not limited to, any combination of Local Area Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), mobile, wired or wireless networks, private networks, or virtual private networks. In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0044] In some embodiments of this disclosure, the optical network communication system described above also includes a server. The server can be a server that provides various services, such as a backend management server that supports devices operating on network nodes. The backend management server can analyze and process received requests and other data, and feed the processing results back to the terminal device.

[0045] Optionally, the server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0046] Those skilled in the art will know that Figure 1 The number of network nodes 10 shown is merely illustrative; any number of network nodes and servers can be used as needed. This disclosure does not limit this.

[0047] The following will describe the exemplary implementation method in detail with reference to the accompanying drawings and embodiments.

[0048] First, this disclosure provides a method for allocating optical network bandwidth resources. This method can be executed by any electronic device with computing capabilities. In the following process, the electronic device is used as an example as a terminal device.

[0049] Figure 2 This illustration shows a flowchart of an optical network bandwidth resource allocation method according to an embodiment of the present disclosure, such as... Figure 2 As shown, the optical network bandwidth resource allocation method provided in this embodiment includes the following steps:

[0050] S202, Obtain network node information for the optical network area to be allocated, wherein the network node information includes: node information and performance information of one or more network nodes within the optical network area to be allocated.

[0051] It should be noted that bandwidth is used to identify the data transmission capacity of a communication line, that is, the highest data rate that can pass through a certain point in the network per unit of time, and the commonly used unit is bps. Bandwidth resources refer to the high-speed transmission resources in a network connection, usually measured in Mbps (megabits per second) or Gbps (gigabits per second). Bandwidth resources are finite. How to allocate bandwidth resources within an optical network area will play a very positive role in enabling operators who control bandwidth resources to collaboratively manage all network nodes, dynamically plan bandwidth resources, save human resources, and reduce network operating costs.

[0052] The optical network area to be allocated is an area in the optical network communication system that requires allocation of optical network bandwidth resources, and includes one or more network nodes. Each network node stores certain data information, namely network node information, including node information and performance information. The node information includes the network node's service information, topology information, geographical location information, and IP address information, etc. The performance information includes information such as the number of bytes sent and received.

[0053] S204: Input the network node information of the optical network area to be allocated into the pre-trained throughput prediction model, and output the predicted throughput of each network node in the optical network area to be allocated.

[0054] It should be noted that throughput refers to the amount of data (measured in bits, bytes, packets, etc.) successfully transmitted per unit of time to a network, device, port, virtual circuit, or other facility. In other words, throughput is the maximum data rate that a device can receive and forward without frame loss.

[0055] Bandwidth determines the maximum potential speed of data movement, while throughput is the actual speed of data movement. Therefore, throughput can be used to determine the bandwidth required by network nodes, thereby enabling the rational allocation of bandwidth resources.

[0056] Throughput prediction models are models that predict the throughput of network nodes. These models are pre-trained modules that collect information from all network nodes within a region and perform multi-task learning to obtain predicted values ​​for all nodes. Underlying data sharing improves learning efficiency. Throughput prediction models can employ mature AI algorithms such as CNN and LSTM. Developing active learning strategies to collect more data further enhances prediction accuracy.

[0057] S206, based on the predicted throughput of each network node in the optical network area to be allocated, allocate bandwidth resources to each network node in the optical network area to be allocated.

[0058] It should be noted that the bandwidth resources within the optical network area to be allocated are limited values. Through the above steps, the throughput of each network node is predicted, that is, the maximum data movement speed of each network node is predicted. Then, based on the predicted throughput of each network node, the existing optical network bandwidth resources are allocated.

[0059] Specifically, without affecting the user experience, bandwidth is allocated to different services on the same optical fiber. Network nodes with lower predicted throughput are allocated less bandwidth resources, while network nodes with higher predicted throughput are allocated more bandwidth resources.

[0060] This embodiment of the disclosure obtains network node information of the optical network area to be allocated, predicts the throughput information of each network node based on this network node information, and allocates bandwidth resources to each network node based on the throughput information of each network node. This rationally allocates the bandwidth resources of the entire area, which is conducive to dynamic planning of bandwidth resources, saves human resources, reduces network operating costs, and promotes the integrated development of optical networks with technologies such as cloud computing and computing power networks.

[0061] In some embodiments of this disclosure, Figure 3 This disclosure illustrates yet another method for allocating optical network bandwidth resources, such as... Figure 3 As shown, the above method also includes the following steps:

[0062] S302, Obtain prediction time configuration information, wherein the prediction time configuration information is used to determine the prediction time period.

[0063] It should be noted that the above method for predicting throughput can also input prediction time configuration information to determine the time period for predicting throughput, thereby measuring throughput data for a certain time period.

[0064] S304, input the network node information of the optical network area to be allocated into the pre-trained throughput prediction model, and output the predicted throughput of each network node in the optical network area to be allocated, including: inputting the prediction time configuration information and the network node information of the optical network area to be allocated into the pre-trained throughput prediction model, and outputting the throughput of each network node in the optical network area to be allocated within the prediction time period.

[0065] This embodiment of the disclosure obtains the predicted time configuration information, enabling the acquisition of the throughput of each network node within the predicted time period. By obtaining throughput information at different times, it allows for greater flexibility in allocating optical network bandwidth resources, enabling dynamic allocation of bandwidth resources, maintaining stable network operation, and improving the flexibility and usability of network bandwidth.

[0066] In some embodiments of this disclosure, the aforementioned prediction time configuration information can also be used to determine the prediction time point. Based on the throughput prediction model trained with historical time information, the possible throughput of a certain network node at a certain time point can be predicted more accurately. Based on the predicted throughput, the bandwidth resource allocation for the network node is dynamically allocated, so that the reasonable allocation of bandwidth resources is maximized. Furthermore, the dynamic allocation of bandwidth resources can be realized to achieve a higher degree of utilization of optical network bandwidth resources.

[0067] In some embodiments of this disclosure, such as Figure 4 As shown, step S206 of the above method, which allocates bandwidth resources to each network node in the optical network area to be allocated based on the predicted throughput of each network node in the optical network area to be allocated, also includes the following steps:

[0068] S402, obtain the confidence threshold pre-configured for each network node.

[0069] It should be noted that the confidence threshold is a pre-configured confidence level of the predicted throughput for each network node, used to characterize the reliability of the predicted throughput value. The confidence threshold is artificially set in this embodiment of the disclosure and is used to determine the confidence level of the predicted throughput; typically, the confidence threshold is set to 95%.

[0070] S404, determine whether the prediction confidence of each network node is greater than the corresponding confidence threshold.

[0071] It should be noted that if the predicted confidence level is greater than the confidence level threshold of the network node, then it can be believed that the predicted throughput of the node is more accurate. That is, if the confidence level of the predicted throughput of the network node and / or the predicted throughput of the network node within the predicted time period is high, the final bandwidth allocation will be more reasonable and there will be no deviation that causes network instability.

[0072] S406, for one or more network nodes with a confidence level greater than the confidence threshold, determine whether the network node meets the preset bandwidth resource allocation conditions.

[0073] It should be explained that when the prediction confidence of each of the aforementioned network nodes exceeds the corresponding confidence threshold, the reliability of the predicted throughput can be ensured. The preset bandwidth resource allocation conditions are other conditions set in advance for allocating more actual bandwidth resources. Allocating bandwidth resources according to these rules will facilitate unified rules and enable various network operators to jointly manage network bandwidth resources and network node information within the same region.

[0074] S408: Based on the predicted throughput of each network node in the optical network area to be allocated, allocate bandwidth resources to one or more network nodes in the optical network area that meet the preset bandwidth resource allocation conditions.

[0075] This disclosure embodiment greatly improves the reliability of the predicted throughput of each network node by setting a confidence threshold for the network node, which is more conducive to the subsequent allocation of bandwidth resources for each network node, making the allocation of optical network bandwidth resources more reasonable and reducing the occurrence of network instability.

[0076] In some embodiments of this disclosure, the above method step S404 determines whether the prediction confidence of each network node is greater than the corresponding confidence threshold. If one or more network nodes are less than the confidence threshold, they can request to obtain more node information of the corresponding network nodes so that the predicted throughput is more accurate, thereby making the prediction confidence greater than the confidence threshold.

[0077] In some embodiments of this disclosure, such as Figure 5 As shown, before determining whether a network node meets the preset bandwidth resource allocation conditions, the following steps are also included:

[0078] S502, identify one or more network nodes that do not meet the preset bandwidth resource allocation conditions.

[0079] It should be noted that when obtaining network node information for each network node, the obtained node information includes some network topology information, service bandwidth information, etc., which can determine whether the network node meets the bandwidth resource allocation conditions.

[0080] S504 outputs node information for each network node that does not meet the preset bandwidth resource allocation conditions.

[0081] The node information of each network node that does not meet the preset bandwidth resource allocation conditions is output to the network node, so that the corresponding network node can respond after seeing the output information.

[0082] This embodiment of the disclosure outputs the node information of one or more network nodes that do not meet the preset bandwidth resource allocation conditions, so that the network nodes in the entire optical network area to be allocated that cannot be allocated bandwidth resources can receive the information in order to deal with the situation where they cannot be allocated bandwidth resources temporarily.

[0083] In some embodiments of this disclosure, when one or more network nodes that do not meet the preset bandwidth resource allocation conditions receive node information that does not meet the preset bandwidth resource allocation conditions, they can request expansion to meet the preset bandwidth resource allocation conditions, thereby enabling flexible allocation of bandwidth resources for the network node when optical network bandwidth resources are allocated.

[0084] In some embodiments of this disclosure, the aforementioned preset bandwidth resource allocation conditions include: whether the network node is a node with an adjustable network topology, and / or whether the predicted throughput of the network node exceeds the corresponding service bandwidth.

[0085] Specifically, the preset bandwidth resource allocation conditions include: whether the network node is a node with an adjustable network topology. If it is adjustable, bandwidth resources are allocated to the network node. If it is not adjustable, node information is output to the network node, and the network node can request an expansion application to realize bandwidth allocation.

[0086] In addition, the preset bandwidth resource allocation conditions also include whether the predicted throughput of network nodes exceeds the corresponding service bandwidth. The service bandwidth information on each network node includes the bandwidth services it has subscribed to. If the actual required service traffic or predicted throughput is higher than the service bandwidth subscribed to by that network node, then node information is output to that network node, enabling it to take measures to resolve the issue of insufficient service bandwidth. This network node can request capacity expansion to further allocate optical network bandwidth resources.

[0087] In some embodiments of this disclosure, the node information in the optical network bandwidth resource allocation method of any of the above claims includes at least one of the following: node geographical location information, node topology information, and node network address information; the performance information includes at least one of the following: data transmission information and data reception information.

[0088] In some embodiments of this disclosure, the collected network node information is preprocessed to obtain input data. Taking the geographical location information and topology information of network nodes as examples, the network node geographic matrix and topology matrix are obtained after preprocessing.

[0089] In addition, preprocessing can also process the data. The collected data may contain omissions or duplicates. Data processing can remove duplicate data and supplement the data that was missed during collection.

[0090] Specifically, the preprocessed data may include, but is not limited to, the following:

[0091] Transmitted bit matrix: The dimension is N×T. Different rows represent the transmitted bit sequences of different network nodes, and different columns represent the transmission performance of network nodes at different time points, where N represents the number of network nodes and T represents the time point.

[0092] Received bit matrix: The dimension is N×T, with different rows representing the received bit sequences of different network nodes, and different columns representing the receiving performance of network nodes at different points in time.

[0093] Granularity: Minimum time period.

[0094] Data collection time: 1×T in dimension, in the form yyyy-mm-dd-hh-mm-ss.

[0095] Network topology matrix: The dimension is N*N, which represents the network topology relationship between different network nodes. The element values ​​corresponding to different topology relationships are shown in Table 1.

[0096] Network connectivity matrix: It has an N×N dimension and a value of 0 or 1, which respectively indicate whether different network nodes are connected.

[0097] Geographic location matrix: with a dimension of N×1, representing the location information of different network nodes.

[0098] Confidence threshold matrix: N×1 in dimension, representing the confidence threshold of different network nodes.

[0099] Bandwidth threshold matrix: N×1 in dimension, representing the service bandwidth threshold for different network nodes.

[0100] Bandwidth matrix: It has an N×1 dimension and a value of 0 or 1, which respectively indicate whether the bandwidth of different network nodes is adjustable.

[0101] Prediction length: The time length M of the predicted traffic data.

[0102] This disclosure defines an input information matrix for an optical network bandwidth resource allocation scheme, which is beneficial for integrating information from different levels and nodes of the optical network, enabling unified data input and processing, reducing human resources, and improving the utilization rate of optical network bandwidth resources.

[0103] In some embodiments of this disclosure, such as Figure 6 As shown, in step 204 of the above method, the network node information of the optical network area to be allocated is input into the pre-trained throughput prediction model, and the predicted throughput of each network node in the optical network area to be allocated is output. The method also includes the following steps:

[0104] S602, Based on the network node information of the optical network area to be allocated, generate a node information matrix and a performance information matrix corresponding to the optical network area to be allocated. The node information matrix contains the node information of each network node in the optical network area to be allocated, and the performance information matrix contains the performance information of each network node in the optical network area to be allocated.

[0105] S604: Input the node information matrix and performance information matrix corresponding to each network node in the optical network area to be allocated into the pre-trained throughput prediction model, and output the predicted throughput matrix and prediction confidence matrix corresponding to the optical network area to be allocated. The predicted throughput matrix contains the predicted throughput of each network node in the optical network area to be allocated, and the prediction confidence matrix contains the predicted confidence of each network node in the optical network area to be allocated.

[0106] The network node information is used to generate a node information matrix and a performance information matrix, and the corresponding predicted throughput matrix and predicted confidence matrix are output based on the node information matrix and performance information matrix.

[0107] The predicted throughput matrix has a dimension of N×M, where different rows represent the predicted throughput sequences of different network nodes, and different columns represent the predicted throughput sequences of network nodes at different times. The predicted confidence matrix also has a dimension of N×M, where different rows represent the predicted confidence of different network nodes, and different columns represent the predicted confidence of network nodes at different time points.

[0108] The acquired network node information is calculated in matrix form, which speeds up the calculation rate of throughput of each network node. The automated collection and data calculation and prediction reduces human resources and integrates information from different levels and different network nodes of the optical network, which plays a positive role in the subsequent allocation of optical network bandwidth resources.

[0109] Based on the same inventive concept, this disclosure also provides an optical network bandwidth resource allocation device, as shown in the following embodiments. Since the principle by which this device solves the problem is similar to that of the above-described method embodiments, the implementation of this device embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0110] Figure 7 This diagram illustrates the structure of an optical network bandwidth resource allocation device according to an embodiment of the present disclosure, as shown below. Figure 7 As shown, the optical network bandwidth resource allocation device 70 includes:

[0111] The information acquisition module 701 is used to acquire network node information of the optical network area to be allocated, wherein the network node information includes: node information and performance information of one or more network nodes in the optical network area to be allocated;

[0112] The throughput prediction module 702 is used to input the network node information of the optical network area to be allocated into the pre-trained throughput prediction model and output the predicted throughput of each network node in the optical network area to be allocated.

[0113] The bandwidth allocation module 703 is used to allocate bandwidth resources to each network node in the optical network area to be allocated based on the predicted throughput of each network node in the optical network area to be allocated.

[0114] In some embodiments of this disclosure, the throughput prediction module 702 is further configured to obtain prediction time configuration information, wherein the prediction time configuration information is used to determine the prediction time period.

[0115] Specifically, the network node information of the optical network area to be allocated is input into a pre-trained throughput prediction model, and the predicted throughput of each network node in the optical network area to be allocated is output. This includes: inputting the prediction time configuration information and the network node information of the optical network area to be allocated into a pre-trained throughput prediction model, and outputting the throughput of each network node in the optical network area to be allocated within the prediction time period.

[0116] In some embodiments of this disclosure, the bandwidth allocation module 703 is further configured to obtain a confidence threshold pre-configured for each network node; determine whether the predicted confidence of each network node is greater than the corresponding confidence threshold; for one or more network nodes that are greater than the confidence threshold, determine whether the network node meets the preset bandwidth resource allocation conditions; and allocate bandwidth resources to one or more network nodes in the optical network area to be allocated that meet the preset bandwidth resource allocation conditions based on the predicted throughput of each network node in the optical network area to be allocated.

[0117] In some embodiments of this disclosure, the bandwidth allocation module 703 is further configured to determine one or more network nodes that do not meet the preset bandwidth resource allocation conditions; and output the node information of each network node that does not meet the preset bandwidth resource allocation conditions.

[0118] In some embodiments of this disclosure, the throughput prediction module 702 is further configured to generate a node information matrix and a performance information matrix corresponding to the optical network region to be allocated, based on the network node information of the optical network region to be allocated. The node information matrix contains node information of each network node in the optical network region to be allocated, and the performance information matrix contains performance information of each network node in the optical network region to be allocated. The node information matrix and the performance information matrix corresponding to each network node in the optical network region to be allocated are input into a pre-trained throughput prediction model, and the predicted throughput matrix and the predicted confidence matrix corresponding to the optical network region to be allocated are output. The predicted throughput matrix contains the predicted throughput of each network node in the optical network region to be allocated, and the predicted confidence matrix contains the predicted confidence of each network node in the optical network region to be allocated.

[0119] In some embodiments of this disclosure, the throughput prediction module 702 further includes a judgment module, which is used to obtain a confidence threshold pre-configured for each network node; determine whether the prediction confidence of each network node is greater than the corresponding confidence threshold; and for one or more network nodes that are greater than the confidence threshold, determine whether the network node meets the preset bandwidth resource allocation conditions.

[0120] If the judgment conditions are met, bandwidth resources are allocated to one or more network nodes in the optical network area to be allocated that meet the preset bandwidth resource allocation conditions, based on the predicted throughput of each network node in the optical network area to be allocated.

[0121] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0122] The following reference Figure 8 To describe an electronic device 800 according to such an embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0123] like Figure 8 As shown, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0124] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 810 can perform the following steps in the above method embodiments: obtaining network node information of the optical network area to be allocated, wherein the network node information includes: node information and performance information of one or more network nodes in the optical network area to be allocated; inputting the network node information of the optical network area to be allocated into a pre-trained throughput prediction model, and outputting the predicted throughput of each network node in the optical network area to be allocated; and allocating bandwidth resources to each network node in the optical network area to be allocated based on the predicted throughput of each network node in the optical network area to be allocated.

[0125] In some embodiments of this disclosure, storage unit 820 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include read-only memory (ROM) 8203.

[0126] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0127] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0128] Electronic device 800 can also communicate with one or more external devices 840 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0129] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0130] Specifically, according to embodiments of this disclosure, the process described above with reference to the flowchart can be implemented as a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the optical network bandwidth resource allocation method described above.

[0131] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. Figure 9 This illustration shows a schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure, such as... Figure 9As shown, the computer-readable storage medium 900 stores a program product capable of implementing the methods described above. In some possible embodiments, various aspects of this disclosure may also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0132] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0134] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0135] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0136] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0137] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0138] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0139] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for allocating bandwidth resources in an optical network, characterized in that, include: Obtain network node information for the optical network area to be allocated, wherein the network node information includes: node information and performance information of one or more network nodes within the optical network area to be allocated; The network node information of the optical network area to be allocated is input into the pre-trained throughput prediction model, and the predicted throughput of each network node in the optical network area to be allocated is output. Based on the predicted throughput of each network node in the optical network area to be allocated, bandwidth resources are allocated to each network node in the optical network area to be allocated. The throughput prediction model is also used to output the prediction confidence of each network node in the optical network area to be allocated; Specifically, bandwidth resource allocation is performed for each network node within the optical network area to be allocated, based on the predicted throughput of each network node within the area to be allocated, including: Obtain the confidence threshold pre-configured for each network node; Determine whether the prediction confidence of each network node is greater than the corresponding confidence threshold; For one or more network nodes with a confidence threshold, determine whether the network nodes meet the preset bandwidth resource allocation conditions; Based on the predicted throughput of each network node in the optical network area to be allocated, bandwidth resources are allocated to one or more network nodes in the optical network area that meet the preset bandwidth resource allocation conditions.

2. The optical network bandwidth resource allocation method according to claim 1, characterized in that, The predicted throughput is the throughput of network nodes within the predicted time period, and the method further includes: Obtain prediction time configuration information, wherein the prediction time configuration information is used to determine the prediction time period; Specifically, inputting the network node information of the optical network area to be allocated into a pre-trained throughput prediction model and outputting the predicted throughput of each network node in the optical network area to be allocated includes: inputting the prediction time configuration information and the network node information of the optical network area to be allocated into a pre-trained throughput prediction model and outputting the throughput of each network node in the optical network area to be allocated within the prediction time period.

3. The optical network bandwidth resource allocation method according to claim 1, characterized in that, Before determining whether a network node meets the preset bandwidth resource allocation conditions, the method further includes: Identify one or more network nodes that do not meet the preset bandwidth resource allocation conditions; Output node information for each network node that does not meet the preset bandwidth resource allocation conditions.

4. The optical network bandwidth resource allocation method according to claim 1, characterized in that, The preset bandwidth resource allocation conditions include: whether the network node is a node with an adjustable network topology, and / or whether the predicted throughput of the network node exceeds the corresponding service bandwidth.

5. The optical network bandwidth resource allocation method according to any one of claims 1 to 4, characterized in that, The node information includes at least one of the following: node geographical location information, node topology information, and node network address information; the performance information includes at least one of the following: data transmission information and data reception information.

6. The optical network bandwidth resource allocation method according to claim 1, characterized in that, The network node information of the optical network region to be allocated is input into a pre-trained throughput prediction model, which outputs the predicted throughput of each network node in the optical network region to be allocated, including: Based on the network node information of the optical network region to be allocated, a node information matrix and a performance information matrix corresponding to the optical network region to be allocated are generated. The node information matrix contains the node information of each network node in the optical network region to be allocated, and the performance information matrix contains the performance information of each network node in the optical network region to be allocated. The node information matrix and performance information matrix corresponding to each network node in the optical network area to be allocated are input into the pre-trained throughput prediction model, and the predicted throughput matrix and prediction confidence matrix corresponding to the optical network area to be allocated are output. The predicted throughput matrix contains the predicted throughput of each network node in the optical network area to be allocated, and the prediction confidence matrix contains the prediction confidence of each network node in the optical network area to be allocated.

7. An optical network bandwidth resource allocation device, characterized in that, The device includes: The information acquisition module is used to acquire network node information of the optical network area to be allocated, wherein the network node information includes: node information and performance information of one or more network nodes in the optical network area to be allocated; The throughput prediction module is used to input the network node information of the optical network area to be allocated into the pre-trained throughput prediction model and output the predicted throughput of each network node in the optical network area to be allocated. The bandwidth allocation module is used to allocate bandwidth resources to each network node in the optical network area to be allocated based on the predicted throughput of each network node in the optical network area to be allocated. The throughput prediction model is also used to output the prediction confidence of each network node in the optical network area to be allocated; The bandwidth allocation module is also used to obtain a confidence threshold pre-configured for each network node; determine whether the predicted confidence of each network node is greater than the corresponding confidence threshold; for one or more network nodes that are greater than the confidence threshold, determine whether the network node meets the preset bandwidth resource allocation conditions; and allocate bandwidth resources to one or more network nodes in the optical network area to be allocated that meet the preset bandwidth resource allocation conditions based on the predicted throughput of each network node in the optical network area to be allocated.

8. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the optical network bandwidth resource allocation method according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the optical network bandwidth resource allocation method according to any one of claims 1 to 6.

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